Electronic training system and method for electronic evaluation and feedback of sports performance

ABSTRACT

An electronic training system includes a set of external response sensors and a set of internal response sensors, and control circuitry. The control circuitry is configured to track locomotion and body movements of a user from the set of external response sensors in a sporting event, and electrical brain activity and physiological changes in a body of the user from the set of internal response sensors in a sporting event. Tracked data in the sporting event is annotated as period-of-relevance and period-of-irrelevance. A first sports performance state is assigned to the user for the sporting event based on a combination of a user feedback and sports statistics. The control circuitry outputs a first integrated visual motion model on a display device based on annotated tracked data in the period-of-relevance such that the first sports performance state of the user for the sporting event is discernible by a viewer.

FIELD OF TECHNOLOGY

Certain embodiments of the disclosure relate to sports performancemonitoring systems and technologies. More specifically, certainembodiments of the disclosure relate to an electronic training systemand method for electronic evaluation and feedback of sports performanceof a user (e.g. a sportsman).

BACKGROUND

Sports have been a meaningful and an integral part of society. Withgreater attention on sports comes greater attention on improving sportsperformance of a given sportsman. Now-a-days, a person intending toprepare for a rewarding sporting career start early in life andspecialize in particular areas and train year-round to improve theirskills. Coaching plays a major role in improving individual and teamperformance that requires the coach to possess an ability to make quickdecisions, supported by an intensive activity of reflection, decisionand guidance to influence sporting performance. Typically, humanobservation and judgment are often prone to biases and it is challengingfor even highly skilled coaches to measure small differences in motionand other factors of the given sportsman in a sporting event, whichadversely affects next phase of training and sporting performance of thegiven sportsman. Currently, there exists many heath and performancemonitoring systems that monitor and provide recommendations to a player.However, the monitoring and recommendations of the conventional systemsare focused to a particular arena of a given sportsman and are oftendisconnected and not in synchronization with training methodologies of acoach of the given sportsman. Thus, a sportsman is usually in a dilemmaregarding which recommendation to follow, what information to implement,and what other to ignore. The biggest challenges faced is informationoverload from such conventional systems, which is confusing rather thanguiding to attain a desired performance. In light of the foregoing,there exists a need for a technical solution that solves theabovementioned problems and enables comprehensive electronic evaluationand convenient feedback of sports performance.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of such systems with some aspects of the present disclosureas set forth in the remainder of the present application with referenceto the drawings.

BRIEF SUMMARY OF THE DISCLOSURE

An electronic training system and method for electronic evaluation andfeedback of sports performance, substantially as shown in and/ordescribed in connection with at least one of the figures, as set forthmore completely in the claims.

These and other advantages, aspects and novel features of the presentdisclosure, as well as details of an illustrated embodiment thereof,will be more fully understood from the following description anddrawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram that illustrates an exemplary environment ofan electronic training system for electronic evaluation and feedback ofsports performance, in accordance with an exemplary embodiment of thedisclosure.

FIG. 2 is a diagram that illustrates different components of a serverarrangement and an electronic training system of FIG. 1, in accordancewith an exemplary embodiment of the disclosure.

FIG. 3A is a diagram that illustrates an exemplary annotation of trackeddata in a sporting event as a set of period-of-relevance and a set ofperiod-of-irrelevance, in accordance with an embodiment of thedisclosure.

FIG. 3B is a diagram that illustrates an exemplary first integratedvisual motion model, in accordance with another embodiment of thedisclosure.

FIG. 3C is a diagram that illustrates an exemplary scenario forimplementation of the electronic training system of FIG. 1, inaccordance with an exemplary embodiment of the disclosure.

FIGS. 4A, 4B, 4C, and 4D collectively, is a flowchart that illustrates amethod for electronic evaluation and feedback of sports performance, inaccordance with an exemplary embodiment of the disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Certain embodiments of the disclosure may be found in an electronictraining system and method for electronic evaluation and feedback ofsports performance. Typically, in conventional systems, the biggestchallenges faced is information overload. As a result of the informationoverload, a sportsman is usually in a dilemma with regards to whichrecommendation to follow, what information to implement, and what otherto ignore, which is very confusing rather than guiding to attain adesired sports performance.

The electronic training system provides an integrated solution thatintegrates a user feedback, for example, feedback from a professionalcoach into technological evaluation of sports performance to be able toachieve a comprehensive and an accurate evaluation of sports performanceof a user. The output from the electronic training system isuser-friendly and is easily discernible by a user. The electronictraining system complements and improves the existing training andfeedback system by pinpointing the areas to focus in a systematicanalysis and feedback of sports performance. Such electronic evaluationand feedback results in improving a current sports performance state ofa user and achieving a target sports performance state. The electronictraining system is highly receptive to various responses generated in abody of a user due to the provisioning of stimulus and can adjust thestimulus as per the responses generated. Further, the electronictraining system is able to provide quantifiable feedback on the progressand performance of the user. The disclosed electronic training systemand method may be used specifically for an individual or for a team forelectronic evaluation and feedback of sports performance. In thefollowing description, reference is made to the accompanying drawings,which form a part hereof, and in which is shown, by way of illustration,various embodiments of the present disclosure.

FIG. 1 is a block diagram that illustrates an exemplary environment ofan electronic training system for electronic evaluation and feedback ofsports performance, in accordance with an exemplary embodiment of thedisclosure. With reference to FIG. 1, there is shown an exemplarynetwork environment 100. The network environment 100 includes a serverarrangement 102, a plurality of electronic training systems 104, aplurality of different stimulus sub-devices 106, a user device 108, acommunication device 110, and a communication network 112.

The server arrangement 102 may include a main Artificial Intelligence(AI)-based system 114. The plurality of electronic training systems 104may include multiple electronic training systems 104 a, 104 b, . . . ,104 n. The electronic training system 104 a may include controlcircuitry 116, a stimulus device 118, a set of internal response sensors120, a set of external response sensors 122, and a local AI-based system124. It will be apparent to those of skill in the art that otherelectronic training systems 104 b, . . . , 104 n are functionallysimilar to the electronic training system 104 a. A plurality of users126 a, 126 b, . . . , 126 n may be associated with the plurality ofelectronic training systems 104. For example, the user 126 a isassociated with the electronic training system 104 a, the user 126 b isassociated with the electronic training system 104 b, and the user 126 nis associated with the electronic training system 104 n. Various devicesin the network environment of the health maintenance system 100 may becommunicatively coupled with each other via the communication network112.

The server arrangement 102 includes suitable circuitry, interfaces,and/or logic configured to instruct the plurality of electronic trainingsystems 104 to provide a plurality of stimuli on various body portionsof the plurality of users 126 a, 126 b, . . . , 126 n. The serverarrangement 102 is further configured to instruct the plurality ofelectronic training systems 104 to sense and measure levels of aplurality of responses generated in the body portions of the pluralityof users 126 a, 126 b, . . . , 126 n due to the application of theplurality of stimuli on the body portions of the plurality of users 126a, 126 b, . . . , 126 n. The server arrangement 102 is furtherconfigured to receive primary information pertaining to a plurality ofstimulus-response pairs from the plurality of electronic trainingsystems 104, based on the measurement of the plurality of responses. Theserver arrangement 102 is further configured to receive, from theplurality of electronic training systems 104, supplementary informationassociated with the plurality of users 126 a, 126 b, . . . , 126 n onwhich the plurality of stimuli was applied. The server arrangement 102may be configured to convert the primary information and thesupplementary information into an AI-based system-readable data format.Examples of the server arrangement 102 may include, but are not limitedto, an application server, a cloud server, a web server, a databaseserver, a mainframe server, or a combination thereof. Further, it shouldbe appreciated that the server arrangement 102 may be a single hardwareserver or a plurality of hardware servers operating in a parallel ordistributed architecture.

For the sake of brevity, operations of each of the plurality ofelectronic training systems 104 are explained with respect to theelectronic training system 104 a. The electronic training system 104 aincludes the local AI-based system 124 that is communicatively coupledto the main AI-based system 114. The electronic training system 104 aincludes suitable logic, circuitry, and/or interfaces configured toannotate tracked data in a sporting event as a set ofperiod-of-relevance and a set of period-of-irrelevance based on acorrelation in the locomotion, body movements, electrical brainactivity, and physiological changes tracked for the user 126 a in thesporting event. The electronic training system 104 a is used forelectronic evaluation and feedback of sports performance for the user126 a. In some embodiments, the electronic training system 104 a may beconfigured to receive control instructions, in a connected mode, fromthe server arrangement 102 for electronic evaluation and to providefeedback of sports performance to the user 126 a. In some embodiments,the electronic training system 104 a may be configured to providefeedback one or more users, such as the user 126 a on its own, in theabsence of online connectivity or when a standalone mode is set at theelectronic training system 104 a.

The plurality of different stimulus sub-devices 106 may correspond tomodular attachments that may be attached to any of the plurality ofelectronic training systems 104, for example, the electronic trainingsystem 104 a, for applying different types of stimuli to the user 126 a.Each of the plurality of different stimulus sub-devices 106 may includesuitable logic, circuitry, and/or interfaces configured to generate astimulus such as a pressure stimulus, a temperature-based stimulus, avibration stimulus, a sound wave stimulus, a virtual reality (VR)stimulus, an odor stimulus, a touch-based stimulus, and a magneticstimulus. Examples of the plurality of different stimulus sub-devices106 are shown in FIG. 2.

The user device 108 may be for an individual, institution, or agencythat provides sports training or coaching services to a sportsman. Forexample, a coach, a dietician, an endurance trainer, and the like, maybe considered the individual that provides the coaching or sportstraining. The institution or agency may be a training and coachingcenter, or a genetic screening laboratory or any entity that providesadvisory services to users. In an implementation, the user device 108may be associated with a coach. Examples of the user device 108 mayinclude, but is not limited to a smartphone, a human machine interface(HMI), a handheld device, a consumer electronic device, and othercomputing device. In some embodiments, the user device 108 may be a partof a machine, for example, a medical equipment.

The communication device 110 may correspond to a telecommunicationhardware (e.g. a relay node or a repeater device). Examples of thecommunication device 110 may include, but are not limited to a5G-capable repeater device, an Evolved-universal terrestrial radioaccess-New radio Dual Connectivity (EN-DC) device, a New Radio(NR)-enabled device, or a mmWave-enabled telecommunication device. Thecommunication device 110 may facilitate communication in both sub 30gigahertz to above 30 gigahertz. In one example, the communicationdevice 110 may receive/transmit the RF signals from/to a base station orfrom another network node.

The communication network 112 may include a medium through which thevarious devices in the network environment, such as the serverarrangement 102, the plurality of electronic training systems 104, theuser device 108, the communication device 110, and the user device 108,may communicate with each other. In some embodiments, a secured anddedicated communication channel may be established between the pluralityof electronic training systems 104 and the server arrangement 102. Thecommunication network 112 may be implemented by use of various wired andwireless communication protocols. Examples of such wired and wirelesscommunication protocols may include, but are not limited to, at leastone of a Transmission Control Protocol and Internet Protocol (TCP/IP),User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), FileTransfer Protocol (FTP), ZigBee, EDGE, IEEE 802.11, light fidelity(Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communication,wireless access point (AP), device to device communication, cellularcommunication protocols, or Bluetooth (BT) communication protocols, or acombination thereof. Other examples of the communication network 112 mayinclude, but are not limited to, the Internet, a cloud network, a LongTerm Evolution (LTE) network, a secured Wireless Local Area Network(WLAN), a Local Area Network (LAN), a telephone line (POTS), or otherwired or wireless network.

The main AI-based system 114 includes suitable circuitry, interfaces,and/or logic configured to train one or more neural network models, forexample, recurrent neural network (RNN), such as Long Short Term Memorynetworks (LSTM) networks, convolution neural network (CNN), deep neuralnetwork (DNN), or an artificial neural network that may be a combinationof the RNN and CNN networks. For example, the main AI-based system 114may train the one or more neural network models to find a relationshipbetween the plurality of stimuli and the plurality of responsesgenerated in the body portions of the users 126 a, 126 b, . . . , 126 n.In accordance with an embodiment, the trained model(s) is then deployedin one or more components of each of the plurality of electronictraining systems 104, for example, the local AI-based system 124. Thedeployed pre-trained neural network model(s) is remotely updatable asand when required. In some embodiments, the server arrangement 102 mayestablish a dedicated and secured link, via the communication network112 or by use of the communication device 110 (e.g. a 5G enabledrepeater device) to update various programmable components, such as thedeployed pre-trained neural network model, of the plurality ofelectronic training systems 104. In an embodiment, the main AI-basedsystem 114 may employ supervised or unsupervised learning model. Themain AI-based system 114 may employ machine learning algorithms, such assupervised, unsupervised, semi-supervised, or reinforcement machinelearning algorithms for operation thereof. Typically, the machinelearning algorithms refer to a category of algorithms employed by asystem that allows the system to become more accurate in predictingoutcomes and/or performing tasks, without being explicitly programmed.

The control circuitry 116 comprises suitable logic, circuitry, andinterfaces configured to process sensor data acquired from the set ofinternal response sensors 120 and the set of external response sensors122. Examples of the control circuitry 116 include anApplication-Specific Integrated Circuit (ASIC) processor, a ComplexInstruction Set Computing (CISC) processor, a combination of a centralprocessing unit (CPU) and a graphics processing unit (GPU), amicrocontroller, and/or other hardware processors.

The stimulus device 118 may correspond to a human senses' stimulatordevice. The stimulus device 118 may comprise suitable logic, circuitry,and/or interfaces configured to applying the plurality of stimuli to thebody portions of the user 126 a to increase sports performance. Examplesof the plurality of stimuli may include, but are not limited to,calibrated pressure, calibrated vibration input, calibrated electricinput, sound waves, magnetic input, a virtual reality or mixed realityenvironment output, and a combination of physical therapy and virtualreality output. The stimulus device 118 may operate under the control ofthe control circuitry 116. In accordance with an embodiment, thestimulus device 118 may include various stimulus sub-devices forproviding different types of stimuli to different body portions of auser. In accordance with another embodiment, the stimulus device 118 mayinclude a plurality of slots (as shown in FIG. 2) to detachably attachthe plurality of different stimulus sub-devices 106 in the plurality ofslots in a modular arrangement.

The set of internal response sensors 120 includes suitable logic,circuitry, and/or interfaces configured to sense and measure a level ofan internal response generated within a body of a user. In accordancewith an embodiment, the set of internal response sensors 120 may beattached to or placed on a body of a user (such as the user 126 a) in anon-invasive manner when the user is undergoing training for a sportingevent or involved in actual sports activities in the sporting event.Examples of the internal responses that may be sensed and measured bythe set of internal response sensors 120 may include, but are notlimited to, electrical brain activity and physiological changes in abody of a user. In one example, the set of internal response sensors 120may comprise an electromyograph for sensing and measuring activity inmuscles and nerves. The set of internal response sensors 120 may furtherinclude a blood pressure monitor, a heart rate monitor, a pulse ratemonitor, a temperature sensor, a low power magnetic resonance imagingsystem, and/or the like. The set of internal response sensors 120 mayoperate under the control of the control circuitry 116.

The set of external response sensors 122 includes suitable logic,circuitry, and/or interfaces configured to sense and measure an externalchange discernible from external surface of the body of a user. Examplesof the external response that may be sensed and measured by the set ofexternal response sensors 122 may include, but are not limited to,locomotion (i.e. movement of individual from one place to another), bodymovements (e.g. movement of joints, limbs, and specific sections of thebody), facial expressions, skin colour, a body posture, gestures, and/orvoice feedback. In one example, the set of external response sensors 122may include an imaging device, a light detection and ranging (LiDAR)sensor, and/or a radio detection and ranging (RADAR) sensor for sensingchanges in facial expressions and gestures of the user when stimulus isprovided to the user. The set of external response sensors 122 mayfurther include an audio sensor for sensing the voice feedback of theuser when the stimulus is provided to the user. The set of externalresponse sensors 122 may operate under the control of the controlcircuitry 116.

In operation, there may be a training phase and an operational phase ofthe electronic training system 104 a. In the training phase, the controlcircuitry 116 of the electronic training system 104 a may be configuredto track movements of each body part of a plurality of body parts of theuser 126 a in relation to a corresponding reference point. The trackingmay occur in a plurality of sporting events from a combination of theset of external response sensors 122 and the set of internal responsesensors 120. The reference point for a specific body part may beselected based on a current position of the specific body part that istracked. In a training phase, depending on the type of a sporting event,a movement dataset from a plurality of movements dataset is retrievedand set as default to be used for reference. For example, if a golfmatch is played, accordingly a movement dataset prespecified for golf isretrieved. The movement dataset has collection of poor to best shots andassociated motion coordinates of many test users and known videoanalysis from other players involved in golf play. The movements ofrotate, tilt, way or manner of holding club face, posture, force appliedin a stroke, etc. may be used as reference. The plurality of movementsdataset may be stored in sports knowledge database (e.g. the sportsknowledge database 208 or 220 in FIG. 2). Similarly, for a differentsport, such as soccer, locomotion movements, i.e. how a player movesfrom one position to another position in a field where the sport isplayed, and the footwork may be more useful than movement of hands.Thus, more emphasis may be provided on legs than on hands tracking.Beneficially, having a specific movement dataset for a specific sportresults in removal of artifacts and noise in tracking, and fine detailsand biomechanics may be captured with less computational processingpower as a result of focused approach in tracking specific body portionsmore precisely.

Additionally, at the time of tracking, different reference points may bedynamically and temporally set for tracking different body parts. Forexample, a left hand and a right hand of the user 126 a may be trackedin relation to a hip joint in a case where the user 126 a is upright innormal standing position. However, on bending or in squat position, areference point in ground (e.g. point of squat) may be dynamically setfrom tracking hand movements in an example. Torso twist (or rotate) maybe tracked from a reference vertical plane (e.g. a coronal plane and asagittal plane of human anatomy). A bend motion may be tracked from atransverse plane. Further, the control circuitry 116 may be furtherconfigured to determine relative motion of one body part from other bodyparts of the plurality of body parts based on the sensor data acquiredform the set of external response sensors 122. Beneficially, thetracking of each body part of the plurality of body parts of the user126 a in relation to a corresponding reference point that isautomatically set as per body posture facilitates enhanced tracking andmotion data capture.

In accordance with an embodiment, the control circuitry 116 in thetraining phase may be further configured to track electrical brainactivity of the user 126 a in the plurality of sporting events by use ofthe set of internal response sensors 120. Typically, neurons in humanbrain communicate through electrical impulses. Such communicationenables the brain to coordinate behaviour, sensation, and emotion. Theelectrical impulses signals are measurable using sensors attached tobrain. There are usually spontaneous oscillations that defines theelectrical activity of the human brain and the variation in theseoscillations defines changes of state and type of brain activity. Manyindividuals for a same action or activity may have same, slightlydifferent, or varied electrical brain activity. Further, same actionunder different circumstances in a sporting event may result indifferent electrical brain activity in same or different areas of brain.Thus, tracking electrical brain activity of individual user over aperiod of time provides a pattern of similarity and variations and whenthese similarity and variations are tagged with resultant performance,for example, good performance, average, or poor performance over aperiod of time, such data becomes powerful tool in electronic analysisof sports psychology just before a sporting event, during sportingevent, and post specified time interval after completion of the sportingevent.

In an example, wearable, whole-scalp electroencephalogram (EEG) and/orfunctional near-infrared spectroscopy (fNIRS) may be embedded in aheadgear, such as a helmet worn by a player, for direct monitoring ofbrain activity during a sporting event or during training, exercise, andpractice for a sporting event. A known technical problem with trackingelectrical brain activity, for example, using EEG is that genuinecerebral data is often contaminated by artifacts of non-cerebral origin,for example, various body movement during exercise and when performingsports activities and/or due to physical exertion in a sporting event.The control circuitry 116 effectively handles such tracking ofelectrical brain activity by masking the effect of such artifacts, toobtain reliable tracked data. The control circuitry 116 is furtherconfigured to tag such electrical brain activity with stationery andmotion parameters (e.g. “acquired when stationary” and “acquired when inmotion”). Beneficially, this provides valuable information of potentialartefacts signal patterns associated with wearable devices that are usedto capture the electrical brain activity, such as EEG, thereby reducesthe risk of misinterpretation during tracking of such electrical brainactivity in a given sporting event. Such signal patterns may be removed(or masked or ignored) from the sensor data related to electrical brainactivity. For example, comparison of amplitudes in alpha and beta bandfor various a time interval before, during and after specified timeinterval of a shot in a sporting event, and associated result of theshot (e.g. good, average, or bad performance) provides throughunderstanding of signals that are associated with good performance, forexample, 90% of time.

In accordance with an embodiment, the control circuitry 116 in thetraining phase may be further configured to track physiological changesinduced in the body of the user 126 a in the plurality of sportingevents by use of the set of internal response sensors 120. Thephysiological changes in the body of a user may refer to changes inactivity in muscles or nerves, blood pressure, heart rate, breathingrate, body temperature, and/or pulse rate. For example, in aerobictraining, weight and volume of a heart increases. In another example,myocardium muscle experience hypertrophy (an enlargement). Further,fiber composition in muscles also changes or certain fibers, such asfast-twitch fibers, are more used in quick and short body movements,such as performed in hockey. The capacity of the capacity of VO2 (volumeof oxygen) also increases with sports endurance. VO2 refers to themaximum rate of oxygen consumption measured during incremental exerciseor sporting activity. Moreover, hormonal, lipid, carbohydratescomposition may also change over a period of time. Further, thephysiological changes induced in the body may further include amount oflactate and glucose in the body. These variables when tracked in theplurality of sporting event for a same user, such as the user 126 a, maybe analyzed to provide a detailed understanding of where the user 126 aneeds to focus to improve sports performance.

In accordance with an embodiment, the control circuitry 116 may beconfigured to store tracking data obtained from the tracked locomotion,the tracked body movements, the tracked electrical brain activity, andthe tracked physiological changes for the user 126 a in each sportingevent of the plurality of sporting event. The control circuitry 116 maybe configured to track and acquire a plurality of variables associatedwith tracked locomotion, the tracked body movements, the trackedelectrical brain activity, and the tracked physiological changes insynchronization with each other. Alternatively stated, individualtracking may not provide reliable and meaningful data, but when trackedin association with other, the artefacts and noise related to differentsensors of the set of internal sensors 120 and the set of externalsensors 122 are minimized or almost nullified.

In accordance with an embodiment, the control circuitry 116 in thetraining phase may be further configured to assign a sports performancestate to the user 126 a at completion of each sporting event of theplurality of sporting events based on a combination of sports statisticsacquired from at least one specified online data source and at least oneof a coach-feedback of the user 126 a or a self-feedback by the user 126a. The electronic training system 104 a takes the advantage of anexpertise of a coach and incorporates such feedback into the electronictraining system 104 a for practical and usable output which is morerelevant for the user 126 a undergoing the sports training as well asthe coach providing the sports training to enhance sports performance.Typically, a sportsman is classified into a beginner stage, anintermediate stage, and a professional stage. Each stage may furtherhave different sports performance state, which may be defined as level 1to level 10, level 1 the lowest performance level of a given stage andthe level 10, the highest level. When a sportsman achieves level 10,next stage is automatically assigned, and the counter is reset tolevel 1. Once the sportsman achieves the level 10 of professional stage,a new stage is created with a difficulty level higher than the previousstage, and the counter of level is reset to level 1. Thus, the sportsperformance state is a combination of a stage and a level assigned tothat stage. The sports performance state not only employs user-feedback(such a coach feedback or a self-feedback of the user 126 a, or acombination of the coach feedback and the self-feedback), but alsoacquires sports statistics of the user 126 a from online informationsource, such as a social network, a sports website, and the like. Thesports statistics may be used to determine whether the sportsperformance feedback from the coach and the self-feedback is not havingany human biases, and also a comparative performance of the user 126 ain relation to other players of a given sporting event is also checked.Based on the combination of the sports statistics, the coach-feedback,and the self-feedback, a more accurate and unbiased sports performancestate may be assigned in each sporting event of the plurality ofsporting event by the control circuitry 116 in the training phase.

In accordance with an embodiment, the control circuitry 116 may beconfigured to store a learned sports performance dataset in the serverarrangement 102 or a local storage system. The learned sportsperformance dataset may include a plurality of historical sportsperformance states and associated tracked data by the set of internalresponse sensors 120 and the set of external response sensors 122. Theplurality of historical sports performance states corresponds to theassigned sports performance state to the user 126 a at completion ofeach sporting event of the plurality of sporting events. The trackeddata for each sporting event is tagged with metadata that facilitatesextraction of tracked data for a specific sporting event from theplurality of sporting events.

In accordance with an embodiment, the control circuitry 116 may befurther configured to annotate tracked data in the sporting event as aset of period-of-relevance and a set of period-of-irrelevance based on acorrelation in the tracked locomotion, the tracked body movements, thetracked electrical brain activity, and the tracked physiological changesfor the user 126 a in each sporting event of the plurality of events. Inan example, the set of period-of-relevance may be determined bysegregation of the tracked data for a given sporting event into shortaction time intervals (i.e. a time interval that defines a specific shotor action in a sporting activity). In an example, the time interval mayinclude an elapsed time period before a shot, during a shot, and aspecified time period after the shot in a tennis sport. Such action timeintervals may be then correlated with other tracked data related to thetracked electrical brain activity, and the tracked physiological changesfor the user 126 a. The remaining data where no relevant action isdetected from all the tracked body movements, the tracked electricalbrain activity, and the tracked physiological changes may be ignored andtagged as the set of period-of-irrelevance. The annotated tracked dataas the set of period-of-relevance for each sporting event along with theassigned sports performance state may be used as training dataset forthe local-AI based system 124. The learned sports performance datasetfurther includes the learnings derived from such annotated tracked dataas the set of period-of-relevance for each sporting event along with theassigned sports performance state.

In accordance with an embodiment, the plurality of electronic trainingsystem 104 may operate in a connected mode or a standalone mode. In theconnected mode, each electronic training system 104 a, 104 b, . . . ,104 n, may be configured to communicate the annotated tracked data asthe set of period-of-relevance for each sporting event along with theassigned sports performance state to the server arrangement. Thereafter,the server arrangement 102 may be configured to extract datapoints astraining dataset from the annotated tracked data that corresponds to theset of period-of-relevance and convert the datapoints into anAI-readable format to feed into the main AI-based system 114. In thestandalone mode, the extraction of datapoints as training dataset fromthe annotated tracked data that corresponds to the set ofperiod-of-relevance and conversion of the datapoints into an AI-readableformat occurs at corresponding electronic training system 104 a, 104 b,. . . , 104 n.

Like the training phase, the control circuitry 116 in the operationalphase may be further configured to track locomotion and body movementsof the user 126 a from the set of external response sensors 122 in asporting event (i.e. a new sporting event). The control circuitry 116may be further configured to track electrical brain activity andphysiological changes in a body of the user 126 a from the set ofinternal response sensors 120 in the sporting event. The controlcircuitry 116 may be further configured to annotate tracked data in thesporting event as a set of period-of-relevance and a set ofperiod-of-irrelevance based on a correlation in the tracked locomotion,the tracked body movements, the tracked electrical brain activity, andthe tracked physiological changes for the user in the sporting event.The control circuitry 116 may be further configured to assign a firstsports performance state to the user 126 a at completion of the sportingevent based on a combination of a user feedback (e.g. a coach feedbackor self-feedback of the user 126 a) and sports statistics acquired fromat least one specified data source (e.g. an online data source).

The control circuitry 116 may be further configured to output a firstintegrated visual motion model on a display device based on annotatedtracked data in the set of period-of-relevance. The integrated visualmotion model comprises a first visual representation of the trackedlocomotion and the body movements, a second visual representation of thetracked electrical brain activity, and a third visual representation ofthe tracked physiological changes in the body that are merged in thefirst integrated visual motion model and time-controlled at output suchthat the first sports performance state of the user 126 a for thesporting event is discernible by a viewer. In accordance with anembodiment, the first integrated visual motion model is athree-dimensional (3D) computer graphic model (hereinafter referred toas 3D model) of the user 126 a that reflects the external as wellinternal changes in a meaningful synchronization during the set ofperiod-of-relevance in the sporting event. For example, in conventionalsystems when a player playing golf plays a stroke, the motion of armsand body posture may be captured by image-capture devices, and may beviewed later in slow-motion to manually evaluate sports performance withrespect to result achieved for that stroke, a ball misses to be holed.However, in conventional systems, many other reasons and causes thatlead to the result are usually ignored. In contrast to the conventionalsystems, the output of the first integrated visual motion model providesa synergistic view of changes in the tracked locomotion and the bodymovements, the tracked electrical brain activity, and the trackedphysiological changes in the body when the player playing golf plays thestroke. In an example, the second visual representation of the trackedelectrical brain activity may be visible within the 3D model of the user126 a. similarly, the third visual representation of the trackedphysiological changes in the body may be visible within the 3D model ofthe user 126 a. In other words, multiple 3D models (the first, second,and third visual representation) are merged with each other to reflectchanges in a synergistic manner. An example of the first integratedvisual motion model is shown and described in FIG. 3B.

In accordance with an embodiment, the control circuitry 116 in theoperational phase may be further configured to output a secondintegrated visual motion model along the first integrated visual motionmodel as feedback. The second integrated visual motion model isoutputted based on a comparison of the assigned first sports performancestate with the stored learned sports performance dataset that comprisesthe plurality of historical sports performance states and associatedtracked data. The second integrated visual motion model indicates a setof positive performance activities (e.g. good performances) and a set ofnegative performance activities (e.g. bad performances) in relation tothe first sports performance state of the user 126 a for the sportingevent.

In accordance with an embodiment, the user 126 a may want to set a goalto reach a target sports performance by constantly challenging self forimprovement in sports performance. The electronic training system 104 aprovides a mechanism by which a user, such as the user 126 a, ischallenged to improve not only technical skills in sports, but alsophycological and tactical training is imparted using various stimulusprovided by the stimulus device 118. The control circuitry 116 may befurther configured to receive an input via a user interface rendered ona display device communicatively coupled to the electronic trainingsystem 104 a. The input comprises a current sports performance state anda target sports performance state of the user 126 a. The input mayinclude user formation pertaining to a current sports performance stateand a target sports performance state that is intended to be achievedfor the user 126 a. In an embodiment, other details, for example,physical characteristics of the user 126 a, a geography, a feedback froma dietician, a current training plan, may also be provided via the userinterface 216 a.

In accordance with an embodiment, the control circuitry 116 may befurther configured to retrieve at least one priori stimulus from asports knowledge database based on the received input. In one example,the priori stimulus may correspond to a stimulus that is known based onexisting knowledge in sports to influence one or more sports performancefactors in the current sports performance state of the user 126 a. Thecontrol circuitry 116 may be further configured to determine a set oftest stimuli specific for the user 126 a based on a combination of thecurrent sports performance state, the target sports performance state,the retrieved at least one priori stimulus, and a trained ArtificialIntelligence (AI) system, such as the local AI-based system 124 or themain AI-based system 114.

In accordance with an embodiment, the control circuitry 116 may befurther configured to control the stimulus device 118 to provide thedetermined set of test stimuli to the user 126 a for a first testduration. The plurality of stimuli may include calibrated pressure,calibrated vibration input, calibrated electric input, calibratedmagnetic input, hot and cold application, touch sense-based input, soundwaves, and/or the like. The plurality of stimuli may further includepresenting a VR-based digital environment. The VR-based digitalenvironment may be a combination of audio effects and visual effects.The control circuitry 116 may be configured to control the stimulusdevice 118 for providing the plurality of stimuli on various bodyportions of the users 126 a. The local AI-based system 124 may provide,to the control circuitry 116, a first output that is indicative of oneor more stimulus parameters and a first test duration for which thedetermined set of test stimuli may be applied to the user 126 a. Basedon the first output, the control circuitry 116 may be configured toactivate the stimulus device 118 for providing the determined set oftest stimuli to the user 126 a for the first test duration. Inaccordance with an embodiment, the control circuitry 116 may beconfigured to activate a single stimulus sub-device or a set of stimulussub-devices from the plurality of different stimulus sub-devices 106 ata given timepoint in the first test duration for providing thedetermined set of test stimuli to the user 126 a.

In accordance with an embodiment, the control circuitry 116 may befurther configured to determine, based on the set of internal responsesensors 120, a first set of responses within the body of the user 126 afrom the provided set of test stimuli. The control circuitry 116 may befurther configured to determine, based on the set of external responsesensors 122, a second set of responses discernible on the body of theuser 126 a from the provided set of test stimuli. As a result of theapplication of the determined set of test stimuli to the user 126 a forthe first test duration, the first set of responses (i.e. internalresponses) may be generated within the body of the user 126 a and thesecond set of responses (i.e. external responses) may be discerniblefrom an external surface of the body of the user 126 a.

Each stimulus of the plurality of stimuli may generate a plurality ofresponses (for example, one or more internal responses and one or moreexternal responses) in the body of the user 126 a. Examples of the oneor more internal responses may include, but are not limited to, nerveconduction, neuron firing, activity in muscles or nerves, activity inbrain, alteration in blood pressure, and/or alteration in pulse rate.Examples of the one or more external responses that are discernible fromexternal surface of the body may include, but are not limited to,locomotion and body movements, change in gestures, a body posture, achange in skin colour, and/or a voice feedback. The control circuitry116 may be further configured to instruct the set of external responsesensors 122 and the set of internal response sensors 120 to sense andmeasure levels of the one or more external responses and one or moreinternal responses generated in the body portions of the user 126 a dueto the application of the plurality of stimuli to the user 126 a. Thecontrol circuitry 116 may be further configured to process sensor dataacquired by the set of internal response sensors 120 and the set ofexternal response sensors 122 pertaining to the first set of responsesand the second set of responses, respectively.

In accordance with an embodiment, the control circuitry 116 may befurther configured to detect which muscle in the body, which area inbrain, or which nerve is responding each provided stimulus of the set oftest stimuli. In other words, if there are any changes in the electricalbrain activity and any physiological change within the body of the user126 a is ascertained based on processing of tracked data. The controlcircuitry 116 may be further configured to quantify the level ofresponse measured by the set of internal response sensors 120.

In accordance with an embodiment, the control circuitry 116 may befurther configured to establish an associative relationship between eachstimulus-response pair from the first set of responses and the secondset of responses using the local AI-based system 124. In someembodiment, for example, in the connected mode, the plurality ofelectronic training systems 104 may be configured to communicate thedetermined plurality of stimulus-response pairs to the serverarrangement 102. Each stimulus-response pair is indicative of a type ofstimulus that was applied and a level of each response that wasgenerated based on the applied stimulus. The plurality of electronictraining systems 104 may be further configured to communicate additionaldetails (such as user information) pertaining to the users 126 a to 126n to the server arrangement 102. The main AI-based system 114 in theconnected mode (or the local AI-based system 124 in the standalone mode)may be configured to establish an associative relationship between eachstimulus-response pair in the plurality of stimulus-response pairs togenerate stimulus-response (SR) pair dataset (as shown in FIG. 2). Inone exemplary scenario, the SR pair dataset may be a tabular databasehaving a plurality of rows and columns. Each row may be associated witha single stimulus and may indicate the level of responses that weregenerated based on the corresponding single stimulus. The main AI-basedsystem 114 in the connected mode (or the local AI-based system 124 inthe standalone mode) may be configured to store the generated SR pairdataset in a memory (such as storage systems).

The control circuitry 116 may be further configured to determine aplurality of causes of similarity and variability based on theestablished associative relationship. The main AI-based system 114 inthe connected mode (or the local AI-based system 124 in the standalonemode) may be configured to determine a plurality of causes of similarityand variability in the plurality of responses based on the establishedassociative relationship between each of the plurality ofstimulus-response pairs, user information, and the available sportsknowledge. Examples of the plurality of causes of similarity andvariability determined by the main AI-based system 114 in the connectedmode (or the local AI-based system 124 in the standalone mode) mayinclude, but are not limited to, age groups, sports endurance under sameconditions, nutrition intake, genomic, body weight, body mass index(BMI), ailment, or the like. The main AI-based system 114 in theconnected mode (or the local AI-based system 124 in the standalone mode)may be further configured to segregate the generated SR pair datasetinto a plurality of training categories, for example a first trainingcategory, a second training category, and a third training category. Thefirst training category may include a first set of stimulus-responsepairs that is generally suitable for a group of people, such as all teammembers of a team. Similarly, the second training category may include asecond set of stimulus-response pairs that is suitable for one or morespecific traits, for example, similar age group, similar sportsperformance issues, similar sports, and/or the like. Likewise, the thirdtraining category may include a third set of stimulus-response pairsthat is suitable for a specific user, i.e. segregated into subsets ofstimulus-response pairs each personalized for a specific user. Inaccordance with an embodiment, the main AI-based system 114 may beconfigured to utilize supervised or unsupervised learning to findrelationships among the plurality of stimulus-response pairs included inthe SR pair dataset for segregation of the SR pair dataset. Thus, allthe stimulus-response pairs included in the SR pair dataset may becategorized and then sub-categorized and a learning may be derived.Based on the segregated SR pair dataset, the main AI-based system 114 istrained to generate a trained neural network model (i.e., the trainedmain AI-based system 114).

The learnings of the trained main AI-based system 114 may be used toupdate the local AI-based system 124, by a transfer-learning operationfrom the trained main AI-based system 114 to the local AI-based system124. For example, the segregated SR pair dataset may be communicatedfrom the server arrangement 102 to each of the plurality of electronictraining systems 104 by the transfer-learning operation. In someembodiments, the trained main AI-based system 114 may be used fordeployment into a new electronic training system 104 a. The mainAI-based system 114 may function as a main AI and the local AI-basedsystem 124 may function as a local AI, which may be updated as and whenrequired by the main AI-based system 114. The local AI-based system 124may be computationally lighter (e.g. having a smaller number of hiddenlayers as compared to the main AI-based system 114).

In accordance with an embodiment, the control circuitry 116 is furtherconfigured to calibrate a set of stimulus parameters for the stimulusdevice 118 based on a combination of the determined first set ofresponses, the second set of responses, the current sports performancestate, the target sports performance state, and the trained artificialintelligence-based system. The stimulus device 118 is re-configured withthe calibrated set of stimulus parameters to apply a new stimulus to theuser 126 a for a second duration. The use of the new stimulus shifts thecurrent sports performance state towards the target sports performancestate. The local AI-based system 124 may be configured to provide asecond output to the control circuitry 116 based on the determination ofthe new stimulus. The second output may indicate the set of stimulusparameters, a second duration for which the new stimulus is to beapplied to the target user 130, and one or more targeted body portionswhere the new stimulus is to be applied. The second duration for whichthe new stimulus is to be applied to the target user 130 may be greaterthan the first test duration. In an embodiment, the new stimulus may notbe available during the training phase. In another embodiment, the newstimulus having the set of stimulus parameters is an existing stimulusthat was available during the training phase. The trained electronictraining system 104 a may be utilized for improving a stamina and sportsperformance of a sportsman by the new stimulus. The trained electronictraining system 104 a may detect the current sports performance state ofa sportsman, and continuously challenge his endurance, fitness,engagement, sports move, and the like, to improve sports performance.

In accordance with an embodiment, the stimulus device 118 may beconfigured to output a first digital environment around the user 126 a.The first digital environment may be a human sense stimulating mixedreality environment that induces a specific stimulus to the user 126 ato increase sports performance from the first spots performance state toa second sports performance state of the user 126 a. In other words, thefirst digital environment may be electronically generated mixed realityenvironment that activates certain areas of brain or de-novo facilitategeneration of well-being chemicals, such as endorphins. The mixedreality environment may be a combination of a virtual environment (e.g.a virtual reality environment) and tangible elements, for example, thatmay provide a stimulus to a human body or a portion of a human body. Inone example, the stimulus device 118 may be used to provide certaintangible stimulus (e.g. heat, smell, pressure, cold, sound, a digitalvisualization) that acts on one or more senses of the plurality of humansenses (5 senses) as a part of the first digital environment, such asaudio-visual environment created by the stimulus device 118. In anotherexample, the stimulus device 118 may be configured to generate magneticfield at different frequency as a part of the first digital environment.In an example, a sub-device of the stimulus device 118, such as a VRscene projecting sub-device, may be used to project audio-visual scenesaround a user to challenge the user 126 a from the current sportsperformance state to the target sports performance state. Theaudio-visual scenes projected by the VR scene projecting sub-device 106h may stimulate hearing and visual senses of the user 126 a.

In accordance with an embodiment, the control circuitry 116 may befurther configured to determine a response to the specific stimulusprovided by the output of the first digital environment around the user126 a. Thus, the local AI-based system 124 may be further tuned to findrelationships as to how an individual is responding to a providedstimulus (or the outputted digital environment). All these informationpieces (i.e. relationships) may be grouped and then sub-grouped and alearning may be derived. For example, it may be found if there exists acorrelation, or no correlation, or less correlation among the analysedinformation. Further, the process of tagging and elimination for eachdata point may be executed to identify correct correlation, inferences,and response(s) to provided stimulus, based on continuous training.Thus, a trained model (i.e. a trained local AI-based system 124) may beobtained.

In accordance with an embodiment, the stimulus device 118 is furtherconfigured to output a second digital environment around the user 126 awith a difficulty level that is greater than the difficulty level of thefirst digital environment. The second digital environment is a humansense stimulating mixed reality environment that induces a new stimulusto the user 126 a to increase sports performance further from the secondsports performance state to a new sports performance state of the user126 a. The second digital environment may be similar to that of thefirst digital environment but may differ in that the second digitalenvironment provides more challenges to the user 126 a to solve. Forexample, the user 126 a may want to set a goal to reach a target sportsperformance by constantly challenging self for improvement in sportsperformance. The electronic training system 104 a provides a mechanismby which a user, such as the user 126 a, is challenged to improve notonly technical skills in sports, but also phycological and tacticaltraining is imparted using various stimulus provided by the stimulusdevice 118. The trained electronic training system 104 a may detect thecurrent sports performance state of a sportsman, and continuouslychallenge his endurance, fitness, engagement, sports move, and the like,to improve sports performance.

In accordance with an embodiment, the control circuitry 116 is furtherconfigured to generate a stimulation instruction pack specific for theuser 126 a based on an output from a trained artificialintelligence-based system. The control circuitry is further configuredto activate a single stimulus sub-device or a set of stimulussub-devices from the plurality of different stimulus sub-devices 106 ata given timepoint in a first duration in accordance with the generatedstimulation instructions pack. The stimulation instruction pack mayinclude a type of control signal for the plurality of different stimulussub-devices 106, a time schedule, an intensity of output, and a set ofsense identifiers. The time schedule may define a specific activationtime and a specific duration to generate the new stimulus in the secondduration by using one or more stimulus sub-devices of the plurality ofdifferent stimulus sub-devices 106 under the control of the stimulusdevice 118. Each sense identifier of the set of sense identifiers mayindicate a unique specific sense stimulating item to be selected foroutput in accordance with the time schedule. For example, a first senseidentifier may indicate a specific smell for output. In such a case, theintensity of output defines what amount of liquid or gas to be sprayedand in which direction. The stimulus device 118 may be configured toselect a unique specific sense stimulating item (for example, an odorgenerating item, a visual effects item, an audio effects item, and atouch-sense based item) for generating a single or multiple sensestimulating output/s to stimulate a specific sense/s of a plurality ofhuman senses based on the type of control signal included in thestimulation instruction pack. Thus, based on the stimulation instructionpack, the stimulus device 118 is re-configured with the calibrated setof stimulus parameters and the new stimulus is applied to at leasttargeted body portions of the user 126 a to enhance the current sportsperformance.

In accordance with an embodiment, the local AI-based system 124 and thecontrol circuitry 116 may continue to improve and personalize thestimulus applied to the user 126 a based the internal responses and theexternal responses exhibited by the user 126 a for achieving the targetsports performance state. In accordance with an embodiment, theelectronic training system 104 a may operate in the connected mode.While in the online mode, the electronic training system 104 a mayoperate under the control of the server arrangement 102. Thus, based onthe setting of the connected mode at the electronic training system 104a, the main AI-based system 114 may be configured to execute the sameoperations as executed by the local AI-based system 124 in thestandalone mode.

In accordance with an embodiment, the control circuitry 116 may befurther configured to determine whether an alteration is required in atraining plan for the current sports performance state of the user 126a, based on the application of the new stimulus to the user 126 a forthe second duration and a shift in the at least one sports performanceindicator of the user 126 a from the current sports performance statetowards the target sports performance state. The control circuitry 116may be further configured to determine a new training plan that isdifferent from the training plan for the current sports performancestate of the user 126 a based on the determination that the alterationis required. The control circuitry 116 may be further configured tocommunicate a training change recommendation for the user 126 a to aprespecified user device 108 of a coach of the user 126 a. The trainingchange recommendation comprises the new training plan for the targetsports performance state and a plurality of sports performanceindicators that indicates the shift from the current sports performancetowards the target sports performance state for the user 126 a.

In an exemplary implementation, the control circuitry 116 may beconfigured to retrieve known sports performance information of aplurality of users (e.g. User 1 and user 2). The known sportsperformance information may be retrieved from a sports knowledgedatabase stored locally in the electronic training system 104 a, fromthe server arrangement 102, or directly parsed from Internet-basedsources. The control circuitry 116 may be further configured to create aVR environment game based on the retrieved known sports performanceinformation of the plurality of users. The VR environment game may thenbe used to train a user to learn new skills.

In another exemplary implementation, the control circuitry 116 may beconfigured to retrieve sports performance information of differentplayers (e.g. players from opposite teams). The control circuitry 116may be further configured to utilize the retrieved sports performanceinformation to create a group-based VR which induces challenges for auser (or opponents) who uses the electronic training system 104 a totrain the user (or opponents). Further, in order to train a team, thecontrol circuitry 116 may be configured to combine the sportsperformance information of selected or all players of the team to createa group-based VR environment for training. In an example, the controlcircuitry 116 may be configured to create a new VR environment byfeeding information for a user who is part of training therapy into theVR environment. For example, if a first user have problems with his/herchild, parent, or a second user known to the first user, the informationrelated to the child, parent, or the second user may be fed into the VRenvironment to create the training therapy for the first user. Inaddition to improvement in sports performance, such training therapy mayalso find application in physical therapy, or other surgical or medicaltraining therapies. Moreover, learned information related to sportsperformance learned from different players by the electronic trainingsystem 104 a may be fed into the VR environment so that all theknowledge (i.e. learnings in terms of technical skills, phycological andtactical skills) may be passed to a user who may be under training.Sports performance for celebrities and famous players (e.g. Messi) isavailable and may be incorporated into the VR environment to create agame or a VR-based gaming environment, which in turn may be used totrain a user, team members of a team, or train opponents of a sport orgame.

FIG. 2 illustrates different components of a server arrangement and anelectronic training system of FIG. 1, in accordance with an exemplaryembodiment of the disclosure. FIG. 2 is described in conjunction withelements from FIG. 1. With reference to FIG. 2, there is shown theserver arrangement 102 and the electronic training system 104 a ofFIG. 1. The server arrangement 102 may further include a controlcircuitry 202, a main storage system 204, and a network interface 206.The main storage system 204 is configured to store a sports knowledgedatabase 208 and learned sports performance (LSP) dataset. Hereinafter,the LSP dataset stored in the main storage system 204 is designated andreferred to as “the LSP dataset 210”. The main AI-based system 114 mayinclude a neural network schema 212.

The electronic training system 104 a may further include a local storagesystem 214, a display 216, and a network interface 218. The localstorage system 214 may store a sports knowledge database 220 and the LSPdataset. Hereinafter, the LSP dataset stored in the local storage system214 is designated and referred to as “the LSP dataset 222”. The display216 may refer to a display device which may be associated with one ormore Uls, such as a UI 216 a. The electronic training system 104 a mayfurther include the plurality of different stimulus sub-devices 106 thatmay be detachably attached to the stimulus device 118 by way of aplurality of slots 224 included in the stimulus device 118. Theplurality of different stimulus sub-devices 106 may include an odoremitter sub-device 106 a, a vibrator sub-device 106 b, a pressuresub-device 106 c, an integrated digital environment (IDE) generatorsub-device 106 d, a magnetic field generator sub-device 106 e, atouch-sense sub-device 106 f, a temperature sub-device 106 g, a VR sceneprojecting sub-device 106 h, and a sound wave control sub-device 106 i.A person of ordinary skill in the art will understand that the serverarrangement 102 and the electronic training system 104 a may alsoinclude other suitable components or systems, in addition to thecomponents or systems which are illustrated herein to describe andexplain the function and operation of the present disclosure.

The control circuitry 202 may comprise suitable logic, circuitry, and/orinterfaces configured to execute instructions stored in the main storagesystem 204. The control circuitry 202 may be configured to implement thetraining phase and the operational phase (as described in FIG. 1) inassociation with the electronic training system 104 a. The controlcircuitry 202 may be configured to generate the LSP dataset 210 in thetraining phase (as described in FIG. 1). The control circuitry 202 maybe configured to convert the primary information and the supplementaryinformation into the AI-based system-readable data format. Examples ofthe control circuitry 202 may be an X86-based processor, a RISCprocessor, an ASIC processor, a CISC processor, a microcontroller, aCPU, a GPU, a state machine, and/or other processors or circuits.

The main storage system 204 may comprise suitable logic, circuitry,and/or interfaces configured to store a machine code and instructionswith at least one code section executable by the control circuitry 202.The main storage system 204 may store the sports knowledge database 208and the LSP dataset 210. The sports knowledge database 208 may includedetails pertaining to available sports literature. For example, thesports knowledge database 208 may include known technical skills, shots,or conditioning required for different outdoor or indoor sports,extracted information from sports performance related e-books, coachingcase studies, research papers, sports psychology, physiology (includingnutrition) focused on preparing a user for competition in a sportingactivity. The main storage system 204 may store one or more machinelearning algorithms (for example, deep learning algorithms or othertypes of artificial intelligence algorithms) that enable the mainAI-based system 114 to implement the training phase based on the sportsknowledge database 208 and the LSP dataset 210 that is specific for aspecific user. Examples of implementation of the main storage system 204may include, but are not limited to, an Electrically ErasableProgrammable Read-Only Memory (EEPROM), a Random Access Memory (RAM), aRead Only Memory (ROM), a Hard Disk Drive (HDD), a Flash memory, aSecure Digital (SD) card, a Solid-State Drive (SSD), and/or a CPU cachememory.

The network interface 206 may comprise suitable logic, circuitry,interfaces, and/or code that may be configured to connect andcommunicate with a plurality of devices in the network environment 100,for example, with the electronic training system 104 a. The networkinterface 206 may implement known technologies to support wirelesscommunication. The network interface 206 may include, but are notlimited to an antenna, a radio frequency (RF) transceiver, one or moreamplifiers, a tuner, one or more oscillators, a digital signalprocessor, a coder-decoder (CODEC) chipset, a subscriber identity module(SIM) card, and/or a local buffer. The network interface 206 maycommunicate via offline and online wireless communication with networks,such as the Internet, an Intranet, and/or a wireless network, such as acellular telephone network, a wireless local area network (WLAN),personal area network, and/or a metropolitan area network (MAN). Thewireless communication may use any of a plurality of communicationstandards, protocols and technologies, such as Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), widebandcode division multiple access (W-CDMA), code division multiple access(CDMA), LTE 4G, 5G, time division multiple access (TDMA), Bluetooth,Wireless Fidelity (Wi-Fi) (such as IEEE 802.11, IEEE 802.11b, IEEE802.11g, IEEE 802.11n, and/or any other IEEE 802.11 protocol), voiceover Internet Protocol (VoIP), Wi-MAX, Internet-of-Things (loT)technology, Machine-Type-Communication (MTC) technology, a protocol foremail, instant messaging, and/or Short Message Service (SMS).

The neural network schema 212 may refer to a neural network architecturehaving a number of layers, such as an input layer, an output layer, andintermediate layers that operates on data received at the input layer togenerate corresponding output at the output layer. The neural networkschema 212 may also be referred to as a neural network model. The neuralnetwork schema 212 of the main AI-based system 114 may be provided withunlabeled, uncategorized data of stimulus-response pairs in the AI-basedsystem-readable data format from the plurality of electronic trainingsystems 104 and the main AI-based system 114 may act on the data toautomatically find structure and pattern in the stimulus-response pairsby extracting features and analyzing its pattern to draw inferences.

The local storage system 214 includes suitable logic, circuitry, and/orinterfaces that may be configured to store machine code and/orinstructions with at least one code section executable by the controlcircuitry 116. The local storage system 214 may store a sports knowledgedatabase 220 and an LSP dataset 222. The sports knowledge database 220may be similar to the sports knowledge database 208 and may includedetails pertaining to the available sports literature. The LSP dataset222 is a local instance of the LSP dataset 210 that is specific for auser, such as the user 126 a. The local storage system 214 may store oneor more machine learning algorithms (for example, deep learningalgorithms or other types of artificial intelligence algorithms) thatenable the local AI-based system 124 to execute one or morecorresponding operations during the connected mode and standalone mode.Examples of implementation of the local storage system 214 may include,but are not limited to, an EEPROM, a RAM, a ROM, an HDD, a Flash memory,an SD card, an SSD, and/or a CPU cache memory.

The display 216 may comprise suitable logic, circuitry, and/orinterfaces configured to receive the user information via the userinterface 216 a and render the first integrated visual motion model forthe user 126 a, who needs to improve sports performance. In accordancewith an embodiment, the display 216 may be a touch screen display thatmay receive an input from the user 126 a or the operator of theelectronic training system 104 a. Examples of the display 216 mayinclude, but are not limited to, a see-through display, aprojection-based display, a smart-glass display, and/or anelectro-chromic display. The display 216 may be a transparent or asemi-transparent display screen. The user interface 216 a may berendered at the display 216 under the control of the control circuitry116.

The network interface 218 may comprise suitable logic, circuitry,interfaces, and/or code that may be configured to connect andcommunicate with a plurality of devices in the network environment 100(FIG. 1). The network interface 218 may be similar to that of thenetwork interface 206.

The plurality of slots 224 may correspond to attachment means for thestimulus device 118 for attaching one or more of the plurality ofdifferent stimulus sub-devices 106 as and when required. For example, ina modular arrangement, the odor emitter sub-device 106 a, the vibratorsub-device 106 b, the pressure sub-device 106 c, the IDE generatorsub-device 106 d, the magnetic field generator sub-device 106 e, thetouch-sense sub-device 106 f, the temperature sub-device 106 g, the VRscene projecting sub-device 106 h, and the sound wave control sub-device106 i may be detachably attached to the stimulus device 118 by using theplurality of slots 224.

The odor (scent) emitter sub-device 106 a may comprise suitable logic,circuitry, and/or interfaces configured to emit different types of odoras output. For example, the odor emitter sub-device 106 a may beconfigured to spray liquid or gas for emitting the different types ofodor. The intensity of the output may be controlled based on an amountof the liquid or gas sprayed. The odor emitted by the odor emittersub-device 106 a may stimulate smelling sense of a user.

The vibrator sub-device 106 b may comprise suitable logic, circuitry,and/or interfaces configured to generate vibrations as output. Theintensity of the output may be controlled by controlling the intensityof generated vibrations. The vibrations generated by the vibratorsub-device 106 b may stimulate touch sense of a user. The pressuresub-device 106 c may comprise suitable logic, circuitry, and/orinterfaces configured to provide calibrated pressure as output. Theintensity of the output may be controlled by controlling the intensityof the pressure. The pressure provided by the pressure sub-device 106 cmay stimulate the touch sense of a user.

The IDE generator sub-device 106 d may comprise suitable logic,circuitry, interfaces, and/or code that may be configured to output acustomized integrated digital environment around a user as output. TheIDE generator sub-device 106 d may control the customized digitalenvironment by use of various modules and devices, of the electronictraining system 104 a. Examples of implementations of the IDE generator106 d may be an X86-based processor, a GPU, a RISC processor, an ASICprocessor, a CISC processor, a microcontroller, a CPU, a specializedhardware generator, and/or other mixed reality control circuits.

The magnetic field generator sub-device 106 e may comprise suitablelogic, circuitry, and/or interfaces configured to generate magneticfield around a user as output. The touch-sense sub-device 106 f maycomprise suitable logic, circuitry, and/or interfaces configured tostimulate touch sense of a user.

The temperature sub-device 106 g may comprise suitable logic, circuitry,and/or interfaces configured to provide hot and cold application to auser. In one example, the temperature sub-device 106 g may include IRlamps for providing hot application to the user.

The VR scene projecting sub-device 106 h may comprise suitable logic,circuitry, and/or interfaces configured to project audio-visual scenesaround a user to challenge the user from a current sports performancestate to a target sports performance state. The audio-visual scenesprojected by the VR scene projecting sub-device 106 h may stimulatehearing and visual senses of the user. Examples of implementations ofthe VR scene projecting sub-device 106 h may be an X86-based processor,a GPU, a RISC processor, an ASIC processor, a CISC processor, amicrocontroller, a CPU, a specialized hardware generator, and/or othercontrol circuits.

The sound wave control sub-device 106 i may comprise suitable logic,circuitry, and/or interfaces configured to generate sound waves asoutput. The sound waves generated by the sound wave control sub-device106 i may stimulate hearing sense of a user. It will be apparent tothose of skill in the art that the plurality of different stimulussub-devices 106 may include other sub-devices as well, for example, anexercise mechanism that enables planned movement in various bodyportions (for example, arms, wrists, legs, thighs, neck, feet, and/orback) of the user for exercising.

FIG. 3A illustrates an exemplary annotation of tracked data in asporting event as a set of period-of-relevance and a set ofperiod-of-irrelevance, in accordance with an embodiment of thedisclosure. FIG. 3A is described in conjunction with elements from FIGS.1 to 2. With reference to FIG. 3A, there is shown a graphicalrepresentation 300A of an exemplary tracked data for an exemplary timeinterval for annotation of the tracked data in a sporting event for thepurpose of electronic evaluation of sports performance of a user (suchas the user 126 a of FIG. 1). In the graphical representation 300A,there is further shown a first signal 302A to denote tracked bodymovements, a second signal 302B to denote tracked electrical brainactivity, and a third signal 302C to denote tracked physiologicalchanges for the user 126 a (not shown) in a given sporting event.

In accordance with an embodiment, the control circuitry 116 may beconfigured to annotate tracked data in the sporting event as a set ofperiod-of-relevance, such as a first period-of-relevance 306A, a secondperiod-of-relevance 306B, and a third period-of-relevance 306C, and aset of period-of-irrelevance, such as a first period-of-irrelevance304A, a second period-of-irrelevance 304B, and a thirdperiod-of-irrelevance 304C. The tracked data is segregated and annotatedinto periods-of-relevance and irrelevance based on a correlation in thetracked body movements, the tracked electrical brain activity, and thetracked physiological changes for the user 126 a in the given sportingevent. The annotated tracked data as the set of period-of-relevance forthe given sporting event may be used as training dataset for thelocal-AI based system 124.

FIG. 3B illustrates an exemplary first integrated visual motion model,in accordance with another embodiment of the disclosure. FIG. 3A isdescribed in conjunction with elements from FIGS. 1, 2, and 3A. Withreference to FIG. 3B, there is shown an exemplary block diagram toexplain a first integrated visual motion model 300B.

In accordance with an embodiment, the control circuitry 116 may beconfigured to output the first integrated visual motion model 300B onthe display 216 based on annotated tracked data in the set ofperiod-of-relevance. The integrated visual motion model 300B comprises afirst visual representation 308 of the tracked locomotion and the bodymovements, a second visual representation 310 of the tracked electricalbrain activity, and a third visual representation 312 of the trackedphysiological changes in the body that are merged in the firstintegrated visual motion model 300B and time-controlled at output suchthat the first sports performance state of the user 126 a for the givensporting event is discernible by a viewer.

In accordance with an embodiment, the first integrated visual motionmodel 300B is a three-dimensional (3D) computer graphic model(hereinafter referred to as 3D model) of the user 126 a that reflectsthe external as well internal changes in a meaningful synchronizationduring the set of period-of-relevance in the given sporting event. Thefirst integrated visual motion model 300B may be a visual scene thatdepicts metadata and meaningful information that indicates variousreasons and causes that led to the first sports performance state of theuser 126 a assigned by the control circuitry 116. The first integratedvisual motion model 300B provides a synergistic view to explore combinedeffects and a holistic view of changes in the tracked locomotion and thebody movements, the tracked electrical brain activity, and the trackedphysiological changes in the body that is easy to understand for aviewer, such as the coach of the user 126 a or the user 126 a. Forexample, when a movement is performed by the user 126 a, for example,kicking a soccer ball, not only the motion of kick, is visible in thefirst visual representation 308, but also some portion of the firstvisual representation is turned transparent so that internal changes,such as blood flow, VO2, heart rate, lactic acid formation in muscle isvisible via the second layer, such as the second visual representation310 as well as electrical brain activity or neuron firing via the thirdvisual representation 312 of the user 126 a is visible too. Further,meaning of such visible data in terms of what is considered a goodperformance and where there is scope of improvement is also determinedand rendered beside the first integrated visual motion model 300B basedon the LSP dataset 222. A reference to previous performance of the user126 a in a previous sporting event, in which the user 126 a wascomparatively better or poor in terms of sports performance state forthe similar type of game situation (e.g. similar motion of limbs duringa kick of the soccer ball) is also automatically retrieved and presentedon the user interface 216 a of the display 216. This provides a holisticand enhanced understanding of the sports performance state for eachrelevant action in the set of period-of-relevance in the given sportingevent.

FIG. 3C illustrates an exemplary scenario for implementation of theelectronic training system of FIG. 1, in accordance with an exemplaryembodiment of the disclosure. FIG. 3C is described in conjunction withelements from FIGS. 1, 2, 3A, and 3B. With reference to FIG. 3C, thereis shown an exemplary scenario 300C that depicts a user interface 314with a first integrated visual motion model 316 along with a secondintegrated visual motion model 316 rendered side-by-side for providingfeedback to the user 126 a to improve sports performance. The firstintegrated visual motion model 316 may correspond to the firstintegrated visual motion model 300B of FIG. 3B. The user interface 314may correspond to the user interface 216 a.

In accordance with an embodiment, the control circuitry 116 in theoperational phase may be further configured to output the secondintegrated visual motion model 316 along the first integrated visualmotion model 314 as feedback via the user interface 314. The secondintegrated visual motion model 316 is outputted based on a comparison ofthe assigned first sports performance state with the stored learnedsports performance dataset 222 (or LSP dataset 210) that comprises theplurality of historical sports performance states and associated trackeddata. The second integrated visual motion model 316 indicates a set ofpositive performance activities and a set of negative performanceactivities in a given sporting event. In this case, the secondintegrated visual motion model 316 is retrieved from the LSP dataset 222or 210 based on a similar action performed to achieve a similar result,such as a goal in a soccer match from a given positions of players in afield. In one example, the second integrated visual motion model 316 maybe one of the sports' performance of the same user 126 a who may haveplayed better in one of previous sporting events as compared to currentsporting event. Thus, a side-by-side visual comparison of the same user126 a acts as an evidence and a practical feedback what works well (i.e.a positive performance activity) and what does not work well (i.e. anegative performance activity) under a similar situation in a sportingevent for the user 126 a. Such

FIGS. 4A, 4B, 4C, and 4D collectively, is a flowchart that illustrates amethod for electronic evaluation and feedback of sports performance, inaccordance with an exemplary embodiment of the disclosure. FIGS. 4A to4D are described in conjunction with elements from FIGS. 1, 2, and 3A to3C. With reference to FIGS. 4A, 4B, 4C, and 4D there is shown aflowchart 400 comprising exemplary operations 402 through 454 executedby the electronic training system 104 a.

At 402, each body part of a plurality of body parts of the user 126 a inrelation to a corresponding reference point may be tracked in a trainingphase in a plurality of sporting events from a combination of the set ofexternal response sensors 122 and the set of internal response sensors120. The reference point for a specific body part may be selected basedon a current position of the specific body part that is tracked. At 404,electrical brain activity of the user 126 a may be tracked in thetraining phase in the plurality of sporting events by use of the set ofinternal response sensors 120.

At 406, physiological changes induced in the body of the user 126 a maybe tracked in the training phase in the plurality of sporting events byuse of the set of internal response sensors 120. At 408, a sportsperformance state may be assigned to the user 126 a at completion ofeach sporting event of the plurality of sporting events based on acombination of sports statistics acquired from at least one specifiedonline data source and at least one of a coach-feedback of the user or aself-feedback by the user 126 a.

At 410, a learned sports performance dataset 210 that comprises aplurality of historical sports performance states and associated trackeddata by the set of internal response sensors 120 and the set of externalresponse sensors 122, may be stored. The learned sports performancedataset may be stored in the local storage system 214 or the serverarrangement 102. The plurality of historical sports performance statescorresponds to the assigned sports performance state to the user atcompletion of each sporting event of the plurality of sporting events.At 412, locomotion and body movements of the user 126 a may be trackedin an operational phase from the set of external response sensors 122 ina first sporting event of the plurality of sporting events (i.e. a newsporting event in the operational phase after the training phase).

At 414, electrical brain activity and physiological changes in a body ofthe user 126 a may be further tracked from the set of internal responsesensors 120 in the first sporting event (i.e. the new sporting event).At 416, tracked data in the first sporting event may be annotated as aset of period-of-relevance and a set of period-of-irrelevance based on acorrelation in the tracked locomotion, the tracked body movements, thetracked electrical brain activity, and the tracked physiological changesfor the user in the first sporting event.

At 418, a first sports performance state may be assigned to the user 126a at completion of the first sporting event (e.g. the new sportingevent) based on a combination of a user feedback and sports statisticsacquired from at least one specified data source. At 420, a firstintegrated visual motion model may be outputted on a display device(e.g. the display 216) based on annotated tracked data in the set ofperiod-of-relevance. The first integrated visual motion model mayinclude a first visual representation of the tracked locomotion and thebody movements, a second visual representation of the tracked electricalbrain activity, and a third visual representation of the trackedphysiological changes in the body that are merged in the firstintegrated visual motion model and time-controlled at output such thatthe first sports performance state of the user 126 a for the sportingevent is discernible by a viewer.

At 422, a second integrated visual motion model may be outputted alongthe first integrated visual motion model as feedback in the operationalphase, based on a comparison of the assigned first sports performancestate with the stored learned sports performance dataset that comprisesthe plurality of historical sports performance states and associatedtracked data. The second integrated visual motion model may indicate aset of positive performance activities and a set of negative performanceactivities in relation to the first sports performance state of the user126 a for the first sporting event (i.e. the new sporting event) or asecond sporting event (e.g. another sporting event different than thefirst sporting event in the operational phase) of the plurality ofsporting events. At 424, an input may be received via a user interface,where the input comprises a current sports performance state and atarget sports performance state of the user 126 a.

At 426, at least one priori stimulus may be retrieved from a sportsknowledge database 220 based on the received input. At 428, a set oftest stimuli specific for the user 126 a may be determined based on acombination of the current sports performance state, the target sportsperformance state, the retrieved at least one priori stimulus, and atrained Artificial Intelligence (AI) system.

At 430, the stimulus device 118 may be controlled to provide thedetermined set of test stimuli to the user 126 a for a first testduration. At 432, based on the set of internal response sensors 120, afirst set of responses may be determined within the body of the user 126a from the provided set of test stimuli.

At 434, based on the set of external response sensors 122, a second setof responses discernible on the body of the user 126 a from the providedset of test stimuli may be determined. At 436, an associativerelationship may be determined between each stimulus-response pair fromthe first set of responses and the second set of responses.

At 438, a plurality of causes of similarity and variability may bedetermined based on the established associative relationship. At 440, aset of stimulus parameters may be calibrated for the stimulus device 118based on a combination of the determined first set of responses, thesecond set of responses, the current sports performance state, thetarget sports performance state, and the trained artificialintelligence-based system (such as the local AI-based system 124 or themain AI-based system 114).

At 442, the stimulus device 118 may be re-configured with the calibratedset of stimulus parameters to apply a new stimulus to the user 126 a fora second duration, where the use of the new stimulus shifts the currentsports performance state towards the target sports performance state. At444, a first digital environment may be outputted around the user 126 a,where the first digital environment may be a human-sense stimulatingmixed reality environment that induces a specific stimulus to the user126 a to increase sports performance from the first spots performancestate to a second sports performance state of the user 126 a.

At 446, a second digital environment may be outputted around the user126 a with a difficulty level that is greater than the difficulty levelof the first digital environment, where the second digital environmentis a human-sense stimulating mixed reality environment that induces anew stimulus to the user 126 a to increase sports performance furtherfrom the second sports performance state to a new sports performancestate of the user 126 a. At 448, a stimulation instruction pack specificfor the user 126 a may be generated based on an output from a trainedartificial intelligence-based system. The control circuitry 116 may befurther configured to activate a single stimulus sub-device or a set ofstimulus sub-devices from the plurality of different stimulussub-devices 106 at a given timepoint in a first duration in accordancewith the generated stimulation instruction pack. The physicalstimulation instructions pack may include the type of control signal forthe plurality of different stimulus sub-devices 106, the time schedulethat defines the specific activation time and the specific duration togenerate the output in the second duration, the intensity of the output,and the set of sense identifiers.

At 450, it may be determined whether an alteration is required in atraining plan for the current sports performance state of the user 126a, based on the application of the new stimulus to the user for thesecond duration and a shift in the at least one sports performanceindicator of the user 126 a from the current sports performance statetowards the target sports performance state. At 452, a new training planmay be determined that is different from the training plan for thecurrent sports performance state of the user 126 a based on thedetermination that the alteration is required.

At 454, a training change recommendation for the user 126 a may becommunicated to a prespecified user device of a coach of the user 126 a,where the training change recommendation comprises the new training planfor the target sports performance state and a plurality of sportsperformance indicators that indicates the shift from the current sportsperformance towards the target sports performance state for the user 126a. The control may pass to end.

The electronic training system 104 a provides a platform by which auser, such as the user 126 a, is challenged to improve not onlytechnical skills in sports, but also phycological and tactical skillsimparted using various stimulus provided by the stimulus device 118. Thetrained electronic training system 104 a may detect the current sportsperformance state of a sportsman, and continuously challenge hisendurance, fitness, engagement, sports move, and the like, to improvesports performance.

In accordance with another embodiment, a deep learning algorithm orneural network model (NM) such as a convolutional neural network (CNNmodel) may be utilized to learn and copy or replicate the actions ofeach player in real life in order to create a corresponding virtualplayer for the virtual environment. Information for real games that aplayer has played may be captured and fed to the deep learning algorithmand used to create the corresponding virtual player. This may be donefor several players. The output from the deep learning algorithm may beused to create and/or program one or more virtual players, which in turnmay be utilized to create the virtual environment. Other examples of thedeep learning algorithm or neural network model may comprise a deepneural network (DNN), a recurrent neural network (RNN), a CNN-recurrentneural network (CNN-RNN), R-CNN, Fast R-CNN, Faster R-CNN, an artificialneural network (ANN), (You Only Look Once) YOLO network, a Long ShortTerm Memory (LSTM) network based RNN, CNN+ANN, LSTM+ANN, a gatedrecurrent unit (GRU)-based RNN, a fully connected neural network, aConnectionist Temporal Classification (CTC) based RNN, a deep Bayesianneural network, a Generative Adversarial Network (GAN), and/or anycombination thereof.

Various embodiments of the disclosure may provide a non-transitorycomputer-readable medium having stored thereon, computer implementedinstruction that when executed by a computing device causes a device toexecute operations similar to the operations disclosed herein for theoperation of the electronic training system 124 a.

While various embodiments described in the present disclosure have beendescribed above, it should be understood that they have been presentedby way of example, and not limitation. It is to be understood thatvarious changes in form and detail can be made therein without departingfrom the scope of the present disclosure. In addition to using hardware(e.g., within or coupled to a central processing unit (“CPU” orprocessor), microprocessor, micro controller, digital signal processor,processor core, system on chip (“SOC”) or any other device),implementations may also be embodied in software (e.g. computer readablecode, program code, and/or instructions disposed in any form, such assource, object or machine language) disposed for example in anon-transitory computer-readable medium configured to store thesoftware. Such software can enable, for example, the function,fabrication, modeling, simulation, description and/or testing of theapparatus and methods describe herein. For example, this can beaccomplished through the use of general program languages (e.g., C,C++), hardware description languages (HDL) including Verilog HDL, VHDL,and so on, or other available programs. Such software can be disposed inany known non-transitory computer-readable medium, such assemiconductor, magnetic disc, or optical disc (e.g., CD-ROM, DVD-ROM,etc.). The software can also be disposed as computer data embodied in anon-transitory computer-readable transmission medium (e.g., solid statememory any other non-transitory medium including digital, optical,analogue-based medium, such as removable storage media). Embodiments ofthe present disclosure may include methods of providing the apparatusdescribed herein by providing software describing the apparatus andsubsequently transmitting the software as a computer data signal over acommunication network including the internet and intranets.

It is to be further understood that the system described herein may beincluded in a semiconductor intellectual property core, such as amicroprocessor core (e.g., embodied in HDL) and transformed to hardwarein the production of integrated circuits. Additionally, the systemdescribed herein may be embodied as a combination of hardware andsoftware. Thus, the present disclosure should not be limited by any ofthe above-described exemplary embodiments, but should be defined only inaccordance with the following claims and their equivalents.

What is claimed is:
 1. An electronic training system, comprising: a setof external response sensors configured to sense and measure an externalchange discernible on a body of a user; a set of internal responsesensors configured to sense and measure an internal change within thebody of the user; and control circuitry in an operational phase isconfigured to: track locomotion and body movements of a user from theset of external response sensors in a first sporting event of aplurality of sporting events; track electrical brain activity andphysiological changes in a body of the user from the set of internalresponse sensors in the first sporting event; annotate tracked data inthe first sporting event as a set of period-of-relevance and a set ofperiod-of-irrelevance based on a correlation in the tracked locomotion,the tracked body movements, the tracked electrical brain activity, andthe tracked physiological changes for the user in the first sportingevent; assign a first sports performance state to the user at completionof the first sporting event based on a combination of a user feedbackand sports statistics acquired from at least one specified data source;and output a first integrated visual motion model on a display devicebased on annotated tracked data in the set of period-of-relevance,wherein the first integrated visual motion model comprises a firstvisual representation of the tracked locomotion and the body movements,a second visual representation of the tracked electrical brain activity,and a third visual representation of the tracked physiological changesin the body that are merged in the first integrated visual motion modeland time-controlled at output such that the first sports performancestate of the user for the first sporting event is discernible by aviewer.
 2. The electronic training system according to claim 1, whereinthe control circuitry in a training phase is further configured to trackmovements of each body part of a plurality of body parts of the user inrelation to a corresponding reference point in the plurality of sportingevents from a combination of the set of external response sensors andthe set of internal response sensors, wherein the reference point for aspecific body part is selected based on a current position of thespecific body part that is tracked.
 3. The electronic training systemaccording to claim 2, wherein the control circuitry in the trainingphase is further configured to: track electrical brain activity of theuser in the plurality of sporting events by use of the set of internalresponse sensors; track physiological changes induced in the body of theuser in the plurality of sporting events by use of the set of internalresponse sensors; and assign a sports performance state to the user atcompletion of each sporting event of the plurality of sporting eventsbased on a combination of sports statistics acquired from at least onespecified online data source and at least one of a coach-feedback of theuser or a self-feedback by the user.
 4. The electronic training systemaccording to claim 3, further comprises a storage system configured tostore a learned sports performance dataset that comprises a plurality ofhistorical sports performance states and associated tracked data by theset of internal response sensors and the set of external responsesensors, wherein plurality of historical sports performance statescorresponds to the assigned sports performance state to the user atcompletion of each sporting event of the plurality of sporting events.5. The electronic training system according to claim 4, wherein thecontrol circuitry in the operational phase is further configured tooutput a second integrated visual motion model along the firstintegrated visual motion model as feedback, based on a comparison of theassigned first sports performance state with the stored learned sportsperformance dataset that comprises the plurality of historical sportsperformance states and associated tracked data, and wherein the secondintegrated visual motion model indicates a set of positive performanceactivities and a set of negative performance activities in relation tothe first sports performance state of the user for the first sportingevent or a second sporting event of the plurality of sporting events. 6.The electronic training system according to claim 1, further comprises astimulus device, wherein the stimulus device is configured to output afirst digital environment around the user, wherein the first digitalenvironment is a human-sense stimulating mixed reality environment thatinduces a specific stimulus to the user to increase sports performancefrom the first spots performance state to a second sports performancestate of the user.
 7. The electronic training system according to claim6, wherein the stimulus device is further configured to output a seconddigital environment around the user with a difficulty level that isgreater than the difficulty level of the first digital environment,wherein the second digital environment is a human-sense stimulatingmixed reality environment that induces a new stimulus to the user toincrease sports performance further from the second sports performancestate to a new sports performance state of the user.
 8. The electronictraining system according to claim 6, wherein the stimulus device is ahuman senses stimulator device, wherein the stimulus device comprises aplurality of slots to detachably attach a plurality of differentstimulus sub-devices in the plurality of slots in a modular arrangement.9. The electronic training system according to claim 6, wherein eachstimulus sub-device of the plurality of different stimulus sub-device isselected from at least one of: a pressure sub-device, a temperaturesub-device, a vibrator sub-device, a sound wave control sub-device, avirtual reality (VR) scene projecting sub-device, an odor emittersub-device, a touch-sense sub-device, a magnetic field generatorsub-device, and an integrated digital environment generator sub-device.10. The electronic training system according to claim 9, wherein thecontrol circuitry is further configured to generate a stimulationinstructions pack specific for the user based on an output from atrained artificial intelligence-based system, wherein the controlcircuitry is further configured to activate a single stimulus sub-deviceor a set of stimulus sub-devices from the plurality of differentstimulus sub-devices at a given timepoint in a first duration inaccordance with the generated stimulation instructions pack.
 11. Theelectronic training system according to claim 6, wherein the controlcircuitry is further configured to: receive an input via a userinterface, wherein the input comprises a current sports performancestate and a target sports performance state of the user; retrieve atleast one priori stimulus from a sports knowledge database based on thereceived input; and determine a set of test stimuli specific for theuser based on a combination of the current sports performance state, thetarget sports performance state, the retrieved at least one prioristimulus, and a trained Artificial Intelligence (AI) system.
 12. Theelectronic training system according to claim 11, wherein the controlcircuitry is further configured to control the stimulus device toprovide the determined set of test stimuli to the user for a first testduration.
 13. The electronic training system according to claim 12,wherein the control circuitry is further configured to: determine, basedon the set of internal response sensors, a first set of responses withinthe body of the user from the provided set of test stimuli; anddetermine, based on the set of external response sensors, a second setof responses discernible on the body of the user from the provided setof test stimuli.
 14. The electronic training system according to claim12, wherein the control circuitry is further configured to: establish anassociative relationship between each stimulus-response pair from thefirst set of responses and the second set of responses; and determine aplurality of causes of similarity and variability based on theestablished associative relationship.
 15. The electronic training systemaccording to claim 13, wherein the control circuitry is furtherconfigured to: calibrate a set of stimulus parameters for the stimulusdevice based on a combination of the determined first set of responses,the second set of responses, the current sports performance state, thetarget sports performance state, and the trained artificialintelligence-based system; and the stimulus device is re-configured withthe calibrated set of stimulus parameters to apply a new stimulus to theuser for a second duration, wherein the use of the new stimulus shiftsthe current sports performance state towards the target sportsperformance state.
 16. The electronic training system according to claim15, wherein the control circuitry is further configured to: determinewhether an alteration is required in a training plan for the currentsports performance state of the user, based on the application of thenew stimulus to the user for the second duration and a shift in at leastone sports performance indicator of the user from the current sportsperformance state towards the target sports performance state; determinea new training plan that is different from the training plan for thecurrent sports performance state of the user based on the determinationthat the alteration is required; and communicate a training changerecommendation for the user to a prespecified user device of a coach ofthe user, wherein the training change recommendation comprises the newtraining plan for the target sports performance state and a plurality ofsports performance indicators that indicates the shift from the currentsports performance towards the target sports performance state for theuser.
 17. A method of electronic evaluation and feedback of sportsperformance, the method comprising: in an electronic training systemthat includes control circuitry: tracking, by the control circuitry,locomotion and body movements of a user from a set of external responsesensors in a first sporting event of a plurality of sporting events;tracking, by the control circuitry, electrical brain activity andphysiological changes in a body of the user from a set of internalresponse sensors in the first sporting event; annotating, by the controlcircuitry, tracked data in the sporting event as a set ofperiod-of-relevance and a set of period-of-irrelevance based on acorrelation in the tracked locomotion, the tracked body movements, thetracked electrical brain activity, and the tracked physiological changesfor the user in the first sporting event; assigning, by the controlcircuitry, a first sports performance state to the user at completion ofthe sporting event based on a combination of a user feedback and sportsstatistics acquired from at least one specified data source; andoutputting, by the control circuitry, a first integrated visual motionmodel on a display device based on annotated tracked data in the set ofperiod-of-relevance, wherein the first integrated visual motion modelcomprises a first visual representation of the tracked locomotion andthe body movements, a second visual representation of the trackedelectrical brain activity, and a third visual representation of thetracked physiological changes in the body that are merged in the firstintegrated visual motion model and time-controlled at output such thatthe first sports performance state of the user for the first sportingevent is discernible by a viewer.
 18. The method according to claim 17,further comprising outputting, by the control circuitry, a secondintegrated visual motion model along the first integrated visual motionmodel as feedback, based on a comparison of the assigned first sportsperformance state with the stored learned sports performance datasetthat comprises the plurality of historical sports performance states andassociated tracked data, and wherein the second integrated visual motionmodel indicates a set of positive performance activities and a set ofnegative performance activities in relation to the first sportsperformance state of the user for the first sporting event or a secondsporting event of the plurality of sporting events.
 19. The methodaccording to claim 17, further comprising outputting, by a stimulusdevice of the electronic training system, a first digital environmentaround the user, wherein the first digital environment is a human-sensestimulating mixed reality environment that induces a specific stimulusto the user to increase sports performance from the first spotsperformance state to a second sports performance state of the user. 20.The method according to claim 19, further comprising outputting, by thestimulus device, a second digital environment around the user having asecond difficulty level that is greater than a first difficulty level ofthe first digital environment, wherein the second digital environment isa human-sense stimulating mixed reality environment that induces a newstimulus to the user to increase sports performance further from thesecond sports performance state to a new sports performance state of theuser.